Pub Date : 2019-04-01DOI: 10.1109/ICWR.2019.8765254
Wasswa Shafik, S. Matinkhah
Web of Things (WoT) allows users to create and share contents through a network. However, the social Web of things (SWoT) is a new concept merging the WoT and social capabilities of the modern internet. These devices are interconnected physical, social, real-time, semantic for better communication by the web. SWoT describes approaches, software architectural styles and programming designs that permit real-world objects to be part of the world communication; however, information privacy is mistreated since information is accessed in uncertainty ways by non-authoritative individuals. In this paper, we review complex privacy issues in SWoT with suggesting state of the art solutions to overcome avoidable scenarios basing on the current surveys, models, and architectures, enlightenment on the drawback’s technology users. We justify that during devices and people interaction to the SWoT through credential assessment for access, content sharing throughout communication and controlling physical things will be the main privacy issues.
物联网(Web of Things, WoT)允许用户通过网络创建和共享内容。然而，社交物联网(SWoT)是一个融合了WoT和现代互联网社交能力的新概念。这些设备是相互连接的物理的、社交的、实时的、语义的，通过网络进行更好的交流。SWoT描述的方法、软件架构风格和编程设计允许现实世界的对象成为世界交流的一部分;然而，由于非权威个人以不确定的方式访问信息，信息隐私受到了滥用。在本文中，我们回顾了SWoT中的复杂隐私问题，并根据当前的调查，模型和架构提出了最先进的解决方案，以克服可避免的情况，对缺点的技术用户的启示。我们认为，在设备和人员通过访问凭证评估与SWoT进行交互的过程中，通过通信共享内容和控制物理事物将成为主要的隐私问题。
{"title":"Privacy Issues in Social Web of Things","authors":"Wasswa Shafik, S. Matinkhah","doi":"10.1109/ICWR.2019.8765254","DOIUrl":"https://doi.org/10.1109/ICWR.2019.8765254","url":null,"abstract":"Web of Things (WoT) allows users to create and share contents through a network. However, the social Web of things (SWoT) is a new concept merging the WoT and social capabilities of the modern internet. These devices are interconnected physical, social, real-time, semantic for better communication by the web. SWoT describes approaches, software architectural styles and programming designs that permit real-world objects to be part of the world communication; however, information privacy is mistreated since information is accessed in uncertainty ways by non-authoritative individuals. In this paper, we review complex privacy issues in SWoT with suggesting state of the art solutions to overcome avoidable scenarios basing on the current surveys, models, and architectures, enlightenment on the drawback’s technology users. We justify that during devices and people interaction to the SWoT through credential assessment for access, content sharing throughout communication and controlling physical things will be the main privacy issues.","PeriodicalId":6680,"journal":{"name":"2019 5th International Conference on Web Research (ICWR)","volume":"14 1","pages":"208-214"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85988640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Combination of utility computing and World Wide Web is base of the cloud computing. The intrinsic features of cloud computing have produced many competitive and computing benefits. The development of cloud computing and the expansion of service providers in this area has led to increase in investment in cloud computing. Large number of users on the one hand and increasing the number of sensitive data on cloud environments on the other hand, caused a dramatic growth in the motivation of malicious activities and as a result of security challenges. Solving the cloud computing security challenges need to proper knowledge of the security issues and the scope of their resolution. Security is a long-term product of interaction between people, process and technology. This categorization is based on possible solutions to security issues. Thus, the division of threats in these three areas can help the managers and security sectors to solve the security problems. Hence, in this paper, along with the comprehensive identification of cyber security challenges, we try to address these threats to categories of people, process, and technologies, in order to find cost effective, efficient and feasible security solutions based on this basis.
{"title":"Cloud Security Issues Based on People, Process and Technology Model: A Survey","authors":"Fariba Ghaffari, Hossein Gharaee, Abouzar Arabsorkhi","doi":"10.1109/ICWR.2019.8765295","DOIUrl":"https://doi.org/10.1109/ICWR.2019.8765295","url":null,"abstract":"Combination of utility computing and World Wide Web is base of the cloud computing. The intrinsic features of cloud computing have produced many competitive and computing benefits. The development of cloud computing and the expansion of service providers in this area has led to increase in investment in cloud computing. Large number of users on the one hand and increasing the number of sensitive data on cloud environments on the other hand, caused a dramatic growth in the motivation of malicious activities and as a result of security challenges. Solving the cloud computing security challenges need to proper knowledge of the security issues and the scope of their resolution. Security is a long-term product of interaction between people, process and technology. This categorization is based on possible solutions to security issues. Thus, the division of threats in these three areas can help the managers and security sectors to solve the security problems. Hence, in this paper, along with the comprehensive identification of cyber security challenges, we try to address these threats to categories of people, process, and technologies, in order to find cost effective, efficient and feasible security solutions based on this basis.","PeriodicalId":6680,"journal":{"name":"2019 5th International Conference on Web Research (ICWR)","volume":"28 1","pages":"196-202"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87390369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/ICWR.2019.8765261
Zahra Farahi, A. Moeini, A. Kamandi
In this paper, we propose new algorithms to improve the performance of recommender systems, based on hierarchical Bloom filters. Since Bloom filters can make a tradeoff between space and time, proposing a new hierarchical Bloom filter causes a remarkable reduction in space and time complexity of recommender systems. Space reduction is due to hashing items in a Bloom filter to manage the sparsity of input vectors. Time reduction is due to the structure of hierarchical Bloom filter. To increase the accuracy of the recommender systems we use Probabilistic version of hierarchical Bloom filter. The structure of hierarchical Bloom filter is B+ tree of order d. Proposed algorithms not only decrease the time complexity but also have no significant effect on accuracy
{"title":"Bloofi Representation for Item/User in Recommender Systems","authors":"Zahra Farahi, A. Moeini, A. Kamandi","doi":"10.1109/ICWR.2019.8765261","DOIUrl":"https://doi.org/10.1109/ICWR.2019.8765261","url":null,"abstract":"In this paper, we propose new algorithms to improve the performance of recommender systems, based on hierarchical Bloom filters. Since Bloom filters can make a tradeoff between space and time, proposing a new hierarchical Bloom filter causes a remarkable reduction in space and time complexity of recommender systems. Space reduction is due to hashing items in a Bloom filter to manage the sparsity of input vectors. Time reduction is due to the structure of hierarchical Bloom filter. To increase the accuracy of the recommender systems we use Probabilistic version of hierarchical Bloom filter. The structure of hierarchical Bloom filter is B+ tree of order d. Proposed algorithms not only decrease the time complexity but also have no significant effect on accuracy","PeriodicalId":6680,"journal":{"name":"2019 5th International Conference on Web Research (ICWR)","volume":"40 1","pages":"67-73"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86990554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the underlying infrastructure of the Internet becomes more advanced and high-speed Internet access becomes more prevalent, the usage of web-based applications increases. Web applications provide users with a wide range of services, and providing a platform for sharing cyber security information is one of those. Studies have shown cyber actors mostly utilize the same techniques, tactics and procedures (TTPs) to attack different companies and platforms. Therefore, sharing threat data, best practices and countermeasures can help every participant in the sharing process to identify potential threats, mitigate their impacts or prevent far-reaching attacks from happening. Establishing a web-based information sharing center have been proved to be beneficial as a communication channel for assisting firms and organizations to promote their own cyber or physical security. Accordingly, in this paper we look at information sharing challenges and different trust models; we also present required technical methods, policies and regulations to overcome the introduced challenges by carefully studying established rules and regulations in the U.S. and European Union. The proposed technical methods, policies and regulations are beneficial for implementing a trustful web-based information sharing center. Finally, future work is outlined.
{"title":"Towards a Functional and Trustful Web-based Information Sharing Center","authors":"Seyedeh Negar Khajeddin, Afsaneh Madani, Hossein Gharaee, Farzaneh Abazari","doi":"10.1109/ICWR.2019.8765297","DOIUrl":"https://doi.org/10.1109/ICWR.2019.8765297","url":null,"abstract":"As the underlying infrastructure of the Internet becomes more advanced and high-speed Internet access becomes more prevalent, the usage of web-based applications increases. Web applications provide users with a wide range of services, and providing a platform for sharing cyber security information is one of those. Studies have shown cyber actors mostly utilize the same techniques, tactics and procedures (TTPs) to attack different companies and platforms. Therefore, sharing threat data, best practices and countermeasures can help every participant in the sharing process to identify potential threats, mitigate their impacts or prevent far-reaching attacks from happening. Establishing a web-based information sharing center have been proved to be beneficial as a communication channel for assisting firms and organizations to promote their own cyber or physical security. Accordingly, in this paper we look at information sharing challenges and different trust models; we also present required technical methods, policies and regulations to overcome the introduced challenges by carefully studying established rules and regulations in the U.S. and European Union. The proposed technical methods, policies and regulations are beneficial for implementing a trustful web-based information sharing center. Finally, future work is outlined.","PeriodicalId":6680,"journal":{"name":"2019 5th International Conference on Web Research (ICWR)","volume":"41 1","pages":"252-257"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90345661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/ICWR.2019.8765271
Parvin Keshvari-Fini, Behrooz Janfada, B. Minaei-Bidgoli
Web tables are worthy sources of relational information. The number of high-quality tables with useful relational information is rapidly increasing to hundreds of millions. Some search engines usually ignore meanings of entities and relationships in indexing thus they have poor performance in tabular data to a suitable field of research is the transformation of web tables into machine-readable knowledge. We first study overview of the use of web tables in different domains then focus on understanding knowledge of web tables. The results indicate that by combining old Information Extraction techniques, and table features and general inference models can extract Knowledge from web tables.
{"title":"A Survey on Knowledge Extraction Techniques for Web Tables","authors":"Parvin Keshvari-Fini, Behrooz Janfada, B. Minaei-Bidgoli","doi":"10.1109/ICWR.2019.8765271","DOIUrl":"https://doi.org/10.1109/ICWR.2019.8765271","url":null,"abstract":"Web tables are worthy sources of relational information. The number of high-quality tables with useful relational information is rapidly increasing to hundreds of millions. Some search engines usually ignore meanings of entities and relationships in indexing thus they have poor performance in tabular data to a suitable field of research is the transformation of web tables into machine-readable knowledge. We first study overview of the use of web tables in different domains then focus on understanding knowledge of web tables. The results indicate that by combining old Information Extraction techniques, and table features and general inference models can extract Knowledge from web tables.","PeriodicalId":6680,"journal":{"name":"2019 5th International Conference on Web Research (ICWR)","volume":"157 1","pages":"123-127"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77133110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/ICWR.2019.8765255
Mehrdad Nasser, Majid Asgari, B. Minaei-Bidgoli
In this paper we use distant supervision for the task of relation extraction from a large corpus in the Persian language. There are supervised and unsupervised methods for relation extraction from text. In supervised relation extraction we use hand labeled corpora. This method suffers from domain dependencies and the difficulties of labeling the text. In unsupervised method, we use large corpora without having to label them but relations extracted using this method cannot be used to populate knowledge bases. Distant supervision takes advantage of large corpora without suffering from domain dependencies and can populate knowledge bases. For our experiment we use FarsBase, a knowledge base containing millions of relation instances, and align entities in 630000 Persian Wikipedia articles to these relation instances and create a distantly supervised dataset. We then extract new relation instances using piecewise convolutional neural networks and compare the results with the baseline model that uses manually extracted features.
{"title":"Distant Supervision for Relation Extraction in The Persian Language using Piecewise Convolutional Neural Networks","authors":"Mehrdad Nasser, Majid Asgari, B. Minaei-Bidgoli","doi":"10.1109/ICWR.2019.8765255","DOIUrl":"https://doi.org/10.1109/ICWR.2019.8765255","url":null,"abstract":"In this paper we use distant supervision for the task of relation extraction from a large corpus in the Persian language. There are supervised and unsupervised methods for relation extraction from text. In supervised relation extraction we use hand labeled corpora. This method suffers from domain dependencies and the difficulties of labeling the text. In unsupervised method, we use large corpora without having to label them but relations extracted using this method cannot be used to populate knowledge bases. Distant supervision takes advantage of large corpora without suffering from domain dependencies and can populate knowledge bases. For our experiment we use FarsBase, a knowledge base containing millions of relation instances, and align entities in 630000 Persian Wikipedia articles to these relation instances and create a distantly supervised dataset. We then extract new relation instances using piecewise convolutional neural networks and compare the results with the baseline model that uses manually extracted features.","PeriodicalId":6680,"journal":{"name":"2019 5th International Conference on Web Research (ICWR)","volume":"1 1","pages":"96-99"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77234483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/ICWR.2019.8765262
Samira Rahimyar Heris, M. Keyvanpour
Considering the importance of software systems in human life, their quality assurance is very important. Fault localization is one of the software testing steps, it tries to find the exact location of fault in code. Most of automatic fault localization techniques use coverage information and results of test cases to calculate the program entities suspiciousness by similarity coefficients. The similarity coefficients designed based on the insight and understanding of developers from software system and they do not have the same performance in different scenarios. To overcome with this problem, we use the Back Propagation neural network and investigate the effect of weighted the neural network to accuracy of locating faults in software programs, because the Back propagation neural network is sensitive to weight and by the initial proper weights to the input layer neurons connections, the search space to achieve optimal weight is decreasing and network accuracy improves. We analyze the effectiveness of the proposed method with randomly weighting the input layer neurons and some basic and efficient similarity coefficients on Siemens suite benchmark. The results show that proposed method has a satisfactory performance for the software fault localization process.
{"title":"Effectiveness of Weighted Neural Network on Accuracy of Software Fault Localization","authors":"Samira Rahimyar Heris, M. Keyvanpour","doi":"10.1109/ICWR.2019.8765262","DOIUrl":"https://doi.org/10.1109/ICWR.2019.8765262","url":null,"abstract":"Considering the importance of software systems in human life, their quality assurance is very important. Fault localization is one of the software testing steps, it tries to find the exact location of fault in code. Most of automatic fault localization techniques use coverage information and results of test cases to calculate the program entities suspiciousness by similarity coefficients. The similarity coefficients designed based on the insight and understanding of developers from software system and they do not have the same performance in different scenarios. To overcome with this problem, we use the Back Propagation neural network and investigate the effect of weighted the neural network to accuracy of locating faults in software programs, because the Back propagation neural network is sensitive to weight and by the initial proper weights to the input layer neurons connections, the search space to achieve optimal weight is decreasing and network accuracy improves. We analyze the effectiveness of the proposed method with randomly weighting the input layer neurons and some basic and efficient similarity coefficients on Siemens suite benchmark. The results show that proposed method has a satisfactory performance for the software fault localization process.","PeriodicalId":6680,"journal":{"name":"2019 5th International Conference on Web Research (ICWR)","volume":"46 1","pages":"100-104"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73696416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/ICWR.2019.8765276
Mahdieh Dehghani, A. Moeini, A. Kamandi
Since the number of facial images has grown in social networks, such as Facebook and Instagram, face recognition has been recognized as one of the important branches of image processing research area, and large databases of face images have been created. As a result, the response time of the face recognition system is recognized as a challenge. Fortunately, dimension reduction techniques help to reduce the number of computations significantly, which directly effects on system response time. As many facial features do not include important information, which is required for getting a better result from the face recognition systems, these techniques are applicable for facial images, as well. Local Feature Hashing (LFH) is a hash-based algorithm that has been used for face recognition. It has shown notable computational improvements over naive search and can overcome some challenges, including recognition of pose, facial expression, illumination, and partial occlusion parameters. With the aim of improving the time that it takes to run the LFH algorithm, we have tested several versions of Locality-Sensitive Hashing (LSH) algorithm. The results showed that some of these algorithms improve the response time of the LFH algorithm. In comparison with the previously conducted research, the number of input images has been increased in our tests. Besides, the number of extracted key-point vectors have been decreased, and the accuracy has not been decreased. In addition, our algorithm is able to overcome the challenge of the existence of foreign objects, such as glass. Among all different hash versions that for the first time used for face recognition, some of them are not recommended for these systems and several functions can provide minimum response time, rather than previous hash-based algorithms.
{"title":"Experimental Evaluation of Local Sensitive Hashing Functions for Face Recognition","authors":"Mahdieh Dehghani, A. Moeini, A. Kamandi","doi":"10.1109/ICWR.2019.8765276","DOIUrl":"https://doi.org/10.1109/ICWR.2019.8765276","url":null,"abstract":"Since the number of facial images has grown in social networks, such as Facebook and Instagram, face recognition has been recognized as one of the important branches of image processing research area, and large databases of face images have been created. As a result, the response time of the face recognition system is recognized as a challenge. Fortunately, dimension reduction techniques help to reduce the number of computations significantly, which directly effects on system response time. As many facial features do not include important information, which is required for getting a better result from the face recognition systems, these techniques are applicable for facial images, as well. Local Feature Hashing (LFH) is a hash-based algorithm that has been used for face recognition. It has shown notable computational improvements over naive search and can overcome some challenges, including recognition of pose, facial expression, illumination, and partial occlusion parameters. With the aim of improving the time that it takes to run the LFH algorithm, we have tested several versions of Locality-Sensitive Hashing (LSH) algorithm. The results showed that some of these algorithms improve the response time of the LFH algorithm. In comparison with the previously conducted research, the number of input images has been increased in our tests. Besides, the number of extracted key-point vectors have been decreased, and the accuracy has not been decreased. In addition, our algorithm is able to overcome the challenge of the existence of foreign objects, such as glass. Among all different hash versions that for the first time used for face recognition, some of them are not recommended for these systems and several functions can provide minimum response time, rather than previous hash-based algorithms.","PeriodicalId":6680,"journal":{"name":"2019 5th International Conference on Web Research (ICWR)","volume":"50 1","pages":"184-195"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80416329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/ICWR.2019.8765290
Mohammad Foad Abdi, Kasra Farrokhi, M. Haeri
Community detection is one of the most important tasks in social networks analysis. This problem becomes more challenging when the structure of the network changes during the time. It is very important to update the structures of the community in a dynamic network without time-consuming procedures. This paper suggests a hybrid evolutionary algorithm for online community detection. The proposed algorithm called Memetic Based Online Community Detection (MBOC) is based on a memetic algorithm with new genetic operators and a novel stochastic local search to assign new nodes to communities and another local search called dense search to modify communities after new assignments. The method is evaluated over several well-known benchmark networks. The results show that the proposed approach outperforms the previous methods in most cases.
社区检测是社交网络分析的重要内容之一。随着时间的推移，网络结构的变化，这个问题变得更加具有挑战性。在不耗费时间的情况下，在动态网络中更新社区结构是非常重要的。提出了一种用于在线社区检测的混合进化算法。Memetic Based Online Community Detection (MBOC)是基于Memetic算法的一种新的遗传算子和一种新的随机局部搜索来分配新节点到社区，以及另一种称为密集搜索的局部搜索来修改新分配后的社区。在几个知名的基准网络上对该方法进行了评估。结果表明，在大多数情况下，该方法优于先前的方法。
{"title":"Memetic Based Online Community Detection","authors":"Mohammad Foad Abdi, Kasra Farrokhi, M. Haeri","doi":"10.1109/ICWR.2019.8765290","DOIUrl":"https://doi.org/10.1109/ICWR.2019.8765290","url":null,"abstract":"Community detection is one of the most important tasks in social networks analysis. This problem becomes more challenging when the structure of the network changes during the time. It is very important to update the structures of the community in a dynamic network without time-consuming procedures. This paper suggests a hybrid evolutionary algorithm for online community detection. The proposed algorithm called Memetic Based Online Community Detection (MBOC) is based on a memetic algorithm with new genetic operators and a novel stochastic local search to assign new nodes to communities and another local search called dense search to modify communities after new assignments. The method is evaluated over several well-known benchmark networks. The results show that the proposed approach outperforms the previous methods in most cases.","PeriodicalId":6680,"journal":{"name":"2019 5th International Conference on Web Research (ICWR)","volume":"69 1","pages":"270-275"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74515958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/ICWR.2019.8765258
Mohammad Moradi, Morteza Moradi, Farhad Bayat
From the very early days, and due to its unprecedented opportunities and advantageous facilities, the Web has been revolutionizing almost every aspects of human life, technology, etc. As a dynamic phenomenon, the Web itself has experienced several major evolution stages. Such fundamental changes never thought to abolish predecessor technologies and concepts but taking them to a new level of functionality. In fact, these paradigm shifts are necessary and inevitable in order to take advantages of state of the art technologies and approaches. In this regard, social Web, semantic Web and similar nomenclatures aimed to emphasize the key idea that best represents the latest achievements and possibly future directions. Thanks to many years of research and development, the current state (version) of the Web puts forward some outstanding opportunities both to make the most of available resources/capabilities and establish the foundation of the future Web. Specifically, convergence of human intelligence (of Web users), machine intelligence (of Web agents) and intelligence of things (through Web of Things) toward shaping Web of Intelligence (WoI) can be regarded as a breakthrough in the field. The major contribution of this work is proposing a Web-based conceptual model for intelligence harvesting (i.e. Web of Intelligence) through which humans, machines and things could collaborate to solve large scale intelligence-intensive problems in a more efficient and effective way. The rationale behind this idea, its building blocks and related considerations are delineated in this paper. Moreover, a novel application based on the proposed concept, entitled comprehensive intelligent search, is introduced.
从早期开始，由于其前所未有的机会和有利的设施，网络已经彻底改变了人类生活、技术等的几乎每一个方面。作为一种动态现象，Web本身经历了几个主要的发展阶段。这种根本性的变化从未想过要废除以前的技术和概念，而是将它们提升到一个新的功能水平。事实上，为了利用最先进的技术和方法，这些范式转换是必要的和不可避免的。在这方面，社交网、语义网和类似的命名旨在强调最能代表最新成就和可能的未来方向的关键思想。由于多年的研究和开发，Web的当前状态(版本)提出了一些突出的机会，既可以充分利用可用的资源/功能，又可以建立未来Web的基础。具体来说，将人类智能(Web用户)、机器智能(Web代理)和物智能(通过物联网)融合在一起，形成智能网络(Web of intelligence, WoI)，可以被视为该领域的突破。这项工作的主要贡献是提出了一个基于Web的智能收集概念模型(即智能网络)，通过该模型，人类，机器和事物可以以更高效和有效的方式协作解决大规模的智能密集型问题。本文描述了这一想法背后的基本原理，其构建模块和相关考虑因素。此外，还介绍了基于该概念的一种新的应用，即综合智能搜索。
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